My girlfriend has been living in New York since May, and we’ve been surviving a long-distance relationship ever since (I spilled my guts out about it back in June.) I spent this past weekend up north visiting and it didn’t last long enough. Fortunately, I brought my Fuji, though we forgot to take any photos the entire time except for this one.

He thinks that by combining qualitative lab data and quantitative real-world data with machine learning, artificial intelligence, and other analytics technologies, he can unlock the secrets that so many of us walking dead are looking for: a better night’s sleep. “We’re operating a huge sleep experiment, worldwide, unlike anything anyone has ever done,” he says. “We have 250 million nights of sleep in our database, and we’re using all the latest technologies to make sense of it.”

Kahn is not alone. He’s part of a movement of brilliant entrepreneurs, data scientists, engineers, and academics who are looking at demographics, geographies, and lifestyles, and even into our genomes. They’re the beneficiaries of a historic explosion in sleep data, and they’re using many of the same technologies that are busily decoding some of the world’s other great mysteries. Tiny sensors, big data, analytics, and cloud computing can predict machine breakage, pinpoint power outages, and build better supply chains. Why not put them to work to optimize the most valuable complex system of all, the human body?

You better believe I loved reading this article from Fortune. It’s a fascinating examination of just about every corner of modern sleep research. The future is here.

The new consumer-oriented bulbs, for example, are designed to regulate the body’s basic need to rest and wake up by stimulating receptors in the eyes that signal to the brain when it is time for bed and when it is time to go about the activities of the day.

When exposed to short-wavelength light, the blue end of the spectrum, those receptors suppress the release of the sleep-inducing hormone melatonin.

Since white artificial light, especially the LEDs used in bulbs and illuminated screens, is typically high in blue, exposure after dusk tends to reduce sleepiness and increase alertness, leading to an epidemic in sleep deficiency, said Dr. Charles A. Czeisler, chief of the Division of Sleep and Circadian Disorders at Brigham and Women’s Hospital in Boston and a professor of sleep medicine at Harvard Medical School.

This stuff isn’t particularly “new” in the scientific literature, but it’s become increasingly present in the public consciousness recently. Looking at light intensity and color is simple low-hanging fruit when addressing sleep issues. (Apps like f.lux and blue-blocking glasses are also proactive solutions that don’t involve expensive house-wide lightbulb replacement.)

For example, isometric exercises like push-ups and spider crawls aren’t accurately tracked because they burn calories at a different rate from standard cardio exercises like running and cycling. If I walk on an elliptical machine for thirty minutes, the Watch will accurately detail the calories burned and the minutes performed. If I’m bench pressing, then it will not accurately calculate the calories I’ve burned. My trainer who’s tried the Watch for himself says that it’s off by a long shot.

Here’s the deal though: people who are doing push-ups or bench pressing aren’t concerned with tracking calories. No one cares. If you’ve read this site for a bit, you sure as hell know I don’t care about calories. The real issue isn’t that it can’t track the calories burned by bench pressing, it’s that it can’t track bench pressing at all.

I found this post via Michael Rockwell’s Initial Charge, who commented:

I’d love to see Apple address these issues, but I’m afraid it would involving having to input the type of exercise, number of reps, etc. into an application. That’s not ideal and requires a much more hands on approach to fitness tracking than cardio workouts do.

The simple fact of the matter is that Apple is, and always has been, catering exclusively to the “long slow distance” (LSD) crowd. The only “fitness” being tracked by the Apple Watch is the dreadful, hour-long misery that is plodding along on an elliptical while you read a magazine or catch up on the latest podcast.

Strength athletes have always tracked their workouts, but even in 2015, most coaches still recommend simple composition notebooks, because no digital alternative has proven to be comprehensive or reliable enough. I think it’s a stretch to say that “having to input the type of exercise, number of reps, etc. into an application” is “not ideal” for the Apple Watch. It’s entirely plausible (I’d design the app myself if…), and it’d only take one well-designed third party app to get me on board the Apple Watch train. If Apple is going to market the Apple Watch as some sort of comprehensive fitness tracker, they’re going to have to include this basic functionality eventually, or lose out on a huge population of athletes searching for a solution (I’d love to track my workouts on my watch, believe me. I despise getting chalk on my iPhone 6 Plus, or even using it in the gym at all.)

I’m very skeptical. In theory, we can recreate all the possible components of a given food — if we could only identify them. With the relatively infantile base of knowledge we currently possess, I don’t think any engineered food powder will contain all the micronutrients we get from real food.

Infant formula has improved by leaps and bounds over the years. They’ve introduced DHA, prebiotics, various specific nutrients like taurine, inositol, and choline, and played with the macronutrients to get it closer and closer to the real thing. Yet it’s stillinferior to breast milk. Now, some time out in the future, maybe we’ll finally pin it down. Maybe parents won’t have to take leave at all. They’ll just strap the kid to the android wetnurse, refill its lab-grown mammary sacs with optimized formula, and head straight back to work. Progress!

I have had so many conversations or email exchanges with students in the last few years wherein I anger them by indicating that simply saying, “This is my opinion” does not preclude a connected statement from being dead wrong. It still baffles me that some feel those four words somehow give them carte blanche to spout batshit oratory or prose. And it really scares me that some of those students think education that challenges their ideas is equivalent to an attack on their beliefs.

A three-part longread from FiveThirtyEight; the first two sections, “Hack” and “Retract”, examine methods that many scientists use to skew data (and study results.) The concluding section, however, is endlessly quotable:

“Science is great, but it’s low-yield,” Fang told me. “Most experiments fail. That doesn’t mean the challenge isn’t worth it, but we can’t expect every dollar to turn a positive result. Most of the things you try don’t work out — that’s just the nature of the process.” Rather than merely avoiding failure, we need to court truth.

Sometimes scientific ideas persist beyond the evidence because the stories we tell about them feel true and confirm what we already believe. It’s natural to think about possible explanations for scientific results — this is how we put them in context and ascertain how plausible they are. The problem comes when we fall so in love with these explanations that we reject the evidence refuting them.

People often joke about the herky-jerky nature of science and health headlines in the media — coffee is good for you one day, bad the next — but that back and forth embodies exactly what the scientific process is all about. It’s hard to measure the impact of diet on health, Nosek told me. “That variation [in results] occurs because science is hard.” Isolating how coffee affects health requires lots of studies and lots of evidence, and only over time and in the course of many, many studies does the evidence start to narrow to a conclusion that’s defensible. “The variation in findings should not be seen as a threat,” Nosek said. “It means that scientists are working on a hard problem.”

(Emphases are mine.)

If you’re short on time, skip straight to part three and read the whole thing, if only to get a good understanding of why science is not easy, why we can’t believe everything we read in the headlines and journals, but also why we should still trust the process.

A bombshell study was just published in the journal Cell Metabolism by Kevin Hall and colleagues and many journalistic publications are—as they are wont to do—misconstruing the results for the sake of flashy headlines and pageviews. This post isn’t meant to be a full-blown literature review, but I do want to bring the study to everyone’s attention.

You can read the full text of the study here if you want (you don’t,) but do realize that this is probably one of the most in-depth, expensive, and meticulously-crafted studies on human energy balance ever done. Nineteen obese adults were locked in a metabolic ward for upwards of two weeks, allowing for precise caloric measurements (among other things) to be taken. The participants were put on a baseline diet for a while, then divided into low-fat and low-carb groups for six days; we return them to baseline, crossover (low-carbers become low-fat, vice-versa), and repeat.

Both diets were what we call isocaloric: all 19 dieters were eating exactly 1918 calories each day, which was exactly a 30% reduction from baseline. The only variables that changed were carbohydrate and fat intake; protein was kept constant. With this setup, Hall could measure changes in body composition knowing precisely what brought them about.

Many journalists are skewing this study as a nail in the coffin for low-carb dieters, citing the authors’ conclusions:

“Calorie for calorie, dietary fat restriction results in more body fat loss than carbohydrate restriction in people with obesity.”

This probably shouldn’t have been the title of the study, because it wasn’t the hypothesis Hall et al. were testing, which instead was:

“Any diet that succeeds does so because the dieter restricts fattening carbohydrates …Those who lose fat on a diet do so because of what they are not eating—the fattening carbohydrates.”

This is a quote out of Gary Taubes’ book Good Calories, Bad Calories, which is worth a read despite the outcome of this study (spoiler alert?) The study, in essence, was designed to test the assertion that for fat loss to occur, it is necessary for carbohydrates to be reduced. Ultimately, this was not the case. Instead, the low-fat group lost a solid pound more of body fat than the low-carb group.

A few obvious criticisms that have already been made:

The low-fat diet was way too low-fat. Sure: only 17 grams of fat per day is fairly unsustainable for most normal human beings. However, the study wasn’t designed to test real-life diets, and to keep calories and protein equal among both populations, this was the necessary step. Which leads to complaint #2…

The low-carb diet wasn’t really low-carb. Also true: the low-carb group was eating 140 grams of CHO/day (few of which were from sugars,) which was indeed a significant reduction from the 350 grams/day of the baseline diet, but still falls well above the cutoff for ketosis and most low-carb diets (which often restricts CHO-intake to below 100g or even 50g/day.) So it shouldn’t be called low-carb, it should instead be called reduced-carb, or moderate-carb. Again, however, this was necessary to keep both diets isocaloric; any further reduction of carbohydrate would end up putting the low-fat group into the negatives!

The study wasn’t long enough. Yeah, six days with each diet is a relatively short time, but it was short enough to measure some changes. More importantly, keeping people locked up in a metabolic ward for two weeks is already difficult enough, not to mention expensive. Hall et al. did what they could here. I’d love to see the same study done over months, but that would require insane monetary commitments and superhuman test subjects. It probably won’t happen.

So again, this study ought not to be interpreted as “low-carb doesn’t work,” nor as “low-fat is superior.” The authors did, however, succeed in proving their original hypothesis: that carbohydrate-reduction is not required for fat-loss to be achieved.

So, how does one reduce fat accumulation if calories aren’t the answer? It turns out that there is but one way for fat to be deposited in your body: the hormone insulin, a hormone released by the pancreas when (and only when) carbohydrates enter the bloodstream (unless, of course, you are diabetic, but that’s another conversation.)

I was, admittedly, being over-simplistic with this statement, and was called out on it. All foods are insulinogenic, otherwise one could eat a zero-carb diet ad libitum and never gain fat at all. The fact of the matter remains that carbohydrates provide a fast-track to insulin overload and the resistance to insulin that accompanies most metabolic diseases. But I was wrong in asserting that this was the only possible way, as this study has demonstrated. (Remembering throughout, of course, that this study didn’t measure real-life diets.)

So why did the low-fat group “win”? My theory, and many others’, is glycogen, the sugars stored in our muscles and livers for use when carb-intake is low (think “carbo-loading” before the big 5K, or some other silliness.) When the reduced-carb group started on their diet, their bodies likely noticed the change and started burning through glycogen to remain “carb-happy.” The low-fat group, on the other hand, continued burning at “high-fat mode” for a while as fat oxidation was downregulated over a few days. (I’m being simplistic here as well for the sake of space. Stephan Guyenet, a man much smarter than myself, goes into more detail on this subject here.)

If you’ve made it this far into the post, you can relax, it’s almost over. Realize again that I am fairly well-versed in scientific reading and writing, and there are others out there smarter than myself with their own interpretations. The case in point is this: it pays to investigate new studies and draw conclusions based on the science, not a journalist’s hasty interpretation of it. The study has not “finally put the low-carb craze to rest” or whatever other crap you’re reading out there.

Update: Some supplemental reading, if you happen to be crazy and enjoy delving into other analyses of this study. My favorite critique is this one from Metabolism and Medicine. It points out some valuable flaws in the study—for instance, accidentally feeding participants the wrong diets on one day, and completely leaving out one woman’s data—and does a much better job of articulating the same points I’ve tried to. This blog post outlines an alternative baseline diet that would’ve put the low-carb arm under 100g/day, more closely resembling a keto-diet and perhaps producing different results.

I was going to give Soylent 2.0 an “F,” but it has not killed anyone yet, so I bumped it up to a “D-.” It contains potentially harmful ingredients; it is nutritionally inadequate, it is bad for your gut, and it tastes like glue. The only advantage it has is speed, but there are other products on the market that are just as fast and are about the same quality. If I were stranded on a desert island, I would drink Soylent 2.0 to stay alive. Other than that, I have no use for it.

This post succinctly sums up most of my issues with the Soylent phenom. John and Jason are both health and nutrition extraordinaires, and they did an excellent job of objectively tearing the drink apart.

This is where stress comes in — much like memory mediates how we interpret and respond to various experiences, a complex set of biological and psychological factors determine how we respond to stress. Some types of stress can be stimulating and invigorating, mobilizing us into action and creative potency; others can be draining and incapacitating, leaving us frustrated and hopeless. This dichotomy of good vs. bad stress, Sternberg notes, is determined by the biology undergirding our feelings — by the dose and duration of the stress hormones secreted by the body in response to the stressful stimulus.

I call them my short-term memory tasks and they are amazingly well suited to Due. There’s one simple reason for this: Due doesn’t ever let me forget. As long as I don’t cheat (by telling Due I did something when I did not do it), Due will pester me until I do what I said I wanted to do.

Non-steroidal anti-inflammatory drugs (NSAIDs) have been associated with increased risk for heart attacks. The U.S. Food and Drug Administration now requires NSAIDs to have labels stating that they increase risk for heart attacks or strokes (FDA Consumer Updates, July 9, 2015). The longer you take them, the greater the risk.

When I first started building this blog, I toyed briefly with using Octopress, but deemed it too complex for my needs. (I ended up going with vanilla Jekyll.) Version 3.0 looks pretty neat, and feature-packed; when it’s finally released, I’ll likely look into it.

Trans fat consumption is a significant contributor to cardiovascular disease. The FDA has long recognized this and finally decided to gradually eliminate it from our food system by 2018. Until then, any industrially produced trans fats still present in our food system should be avoided, though this can be quite difficult due to confusing and misleading nutritional labels.

Trans fats are perhaps the only compound found in our foods that are unanimously, objectively bad for you. (Almost.)

The individual muscles that make this happen are not of concern during the execution of a record performance, unless one of them fails due to injury.

Strength training has long been the victim of a lack of focus on the movement patterns of the segments of body itself, in lieu of the great deal of attention being paid to the constituent components - the “muscle groups” of bodybuilding-think.

Let’s examine your favorite and mine, the deadlift, from the perspective of rigid-body analysis, and see if we can’t come to a better understanding of what actually happens when a bar is pulled from the floor.

A very lengthy, very dry analysis of one of the most important exercises anyone can (and should) do.

Pediatric bone specialists know that the skeletal characteristics acquired in the adolescent and teenage phases of development are carried forward into adulthood. Stronger and bigger teenage bones beget stronger and bigger adult bones. A thicker teenage sub-cartilaginous bone layer — under the hip joint cartilage, for example — acquired through the stress of loaded work, play, and exercise is a thicker adult sub-cartilaginous bone layer, and a hip that is more resistant to osteoarthritis than that of a lazy kid/sedentary adult.

It has been my experience that most practicing pediatricians don’t know this. Most pediatricians advise children and their parents that kids should avoid lifting weights, under the pretense that it damages young joints or, for God’s sake, stunts the growth.

I once had a kid — a large, not-very-explosive kid — who was told by his pediatrician, “I’d hate to see you jeopardize your career in sports by lifting a bunch of heavy weights.” This is a comically tragic miscarriage of professional authority, and very bad advice.